Hierarchical Self-Supervised Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans

Liu, Zuozhu; He, Xiaoxuan; Wang, Hualiang; Xiong, Huimin; Zhang, Yan; Wang, Gaoang; Jiang, Hao; Feng, Yaokai; Zhu, Fang; Hu, Haoji

doi:10.1109/tmi.2022.3222388

Cited by 15 publications

(2 citation statements)

References 43 publications

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“…Utilizing the GNN allows direct processing of gridded data, speeding up the inference process and achieving a 97.49% mIoU and a 98.94% accuracy on a private dental dataset. Liu et al 69 proposed a self-supervised learning framework for 3D tooth segmentation, called STSNet. The framework is divided into two stages: pretraining and fine-tuning.…”

Section: D Tooth Segmentation Methods Based On Gcnsmentioning

confidence: 99%

Deep learning-based tooth segmentation methods in medical imaging: A review

Chen,

Ma,

et al. 2024

Proc Inst Mech Eng H

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Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.

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Section: D Tooth Segmentation Methods Based On Gcnsmentioning

confidence: 99%

Deep learning-based tooth segmentation methods in medical imaging: A review

Chen,

Ma,

et al. 2024

Proc Inst Mech Eng H

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show abstract

“…In addition, Im et al proposed a dynamic-graph convolutional neural network (DGCNN) to automate tooth segmentation in digital models, achieving superior accuracy and reduced computational time compared to the other two commercially available pieces of software: OrthoAnalyzer (ver.1.7.1.3) and Autolign (ver.1.6.2.1) [79]. Beyond that, the accurate segmentation of teeth and the recognition of landmarks on teeth are crucial for automated dental analysis, and significant advancements have been consistently achieved in this domain, hopefully paving the way for further clinical applications [80][81][82][83][84].…”

Section: Dental Analysismentioning

confidence: 99%

Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

Liu,

Zhang,

Shan

2023

Healthcare

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In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.

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Deep Learning in Image Processing: Part 1—Types of Neural Networks, Image Segmentation

Pauwels,

Iosifidis

2023

Artificial Intelligence in Dentistry

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Hierarchical Self-Supervised Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans

Cited by 15 publications

References 43 publications

Deep learning-based tooth segmentation methods in medical imaging: A review

Deep learning-based tooth segmentation methods in medical imaging: A review

Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

Deep Learning in Image Processing: Part 1—Types of Neural Networks, Image Segmentation

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